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Guide to Building a Data Strategy Framework


Your data strategy is only as good as the framework supporting it. If you don’t have a framework in the first place, you won’t have a strategy. This isn’t something that is necessarily complicated to plan out, but it can be confusing to know what exactly constitutes a good data foundation. With that in mind, below […]

The post Guide to Building a Data Strategy Framework appeared first on DATAVERSITY.


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Author: Anas Baig

Why the Cloud-Native Enterprise Is the Future of Business


The pandemic has been a test for many businesses, especially when it comes to their agility, adaptability, and resilience. The ongoing crisis has shown that organizations must be equipped to rapidly respond to unexpected crises – be they cyberattacks, natural disasters, or even a global pandemic. To add to these, businesses constantly face a myriad […]

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Author: Kevin Davis

GenZ’s surprising love for a brand

Pew Research Center has been studying the Millennial generation for years but in 2018 decided there was a difference between Millennials and the next generation.

They decided to use 1996 as the last birth year for Millennials for their future work. Anyone born between 1981 and 1996 was considered a Millennial, and anyone born from 1997 onward is part of a new generation.

They hesitated at first to give this next generation a name but settled on Generation Z. In the interceding years, Gen Z has taken hold in popular culture and journalism. You will find it referenced in Merriam-Webster, Oxford, and Urban Dictionary as the generation that follows Millennials.

This is a generation with which you have an 8-second window to either perform or perish.

Sarwant Singh – Forbes

As cited in a 2019 article, “generational cutoff points aren’t an exact science. They should be viewed primarily as tools”, tools for analysis and classification of people, like your customers!

Why do we bring this up? Well, the answer is simple, Gen Z represents a significant customer market and they are a generation that has been raised on the internet and social media.

Most of them earn their own income and even if they haven’t quite flown the coop, their parents and family members likely support them financially with more robust purchasing power than prior generations of youth.

In China, for example, they have naturally become the main force that drives China’s consumer market.

As members of Gen Z take center stage in the consumer market, they are influencing the survivorship of brands and they aren’t necessarily going for the mainstream or traditional brands, and China, in particular, not even necessarily chasing global or foreign brands.

This represents a significant opportunity for newcomers, niche brands, and brands that choose to distinguish themselves from the rest of the pack.

“Broadly speaking, Gen Zers are ethnically diverse, socially aware, and environmentally conscious” according to Singh. Authenticity and transparency are key and they prefer direct autonomy and control in their decision-making as opposed to being ‘sold to’. This implies a general distrust of big established brands, and favor for brands that focus on the individual. Purchase decisions are therefore based on peer reviews, accessible product information, and ratings – decision potentially by consensus. Who has all that? Actually, the heritage brands do, they just need to keep the volume and velocity of good ratings up.

Where global brands sometimes fail

One has to acknowledge that tastes change over time, and with them, brand preferences. Just ten years ago, if you were considering the purchase of a Tesla car say, you would have raised some eyebrows, today, not so much.

The eyebrows would likely have been raised by those born in an era that was dominated by global brands like Ford, Volkswagen, Hyundai, Nissan, Toyota, GM, Mazda, and the like. Other brands like Mercedes-Benz, BMW, Porsche, Ferrari, Lamborghini, Bentley, and Rolls Royce would have been regarded as premium brands. You’ll find a lot of sentiment out there about these brands because many of them have been multi-generationally familiar to us.

An article in the SCMP suggests â€œYour choice between, say, an Audi Q7, a BMW X5, a Porsche Cayenne or a Mercedes GLE (now) becomes more a matter of your brand preference than of significant product differences. The choice of options and materials is pretty much the same.” Engines used to be a differentiator, for example, but that difference may be moot with all-electric engines, and though the heritage brands are late to the game, they may still have their brand and their history with the past customers as the ultimate trump card.

Studies undertaken in 2020 and 2021 by YPulse in North America according to Inc, noted that young people were already thinking differently about mobility for example. Gen Z’ers are looking at alternatives to ride-share and public transport in favor of their own rides. 3.4% no longer want to ride public transport and 56% say they want safer transportation, options that they obviously feel ridesharing and public transport don’t necessarily offer.

E&Ys 2020 Mobility Consumer Index found that 45% of all first-time car buyers are Millennials, another powerhouse group with dollars to spend. Intuitively, one would expect the more ‘environment woke’ Gen Z and Millennial consumers would be interested in only all-electric vehicles like those offered initially by just Tesla and a few others, the analysis doesn’t necessarily support this viewpoint though.

Based on market research performed by Hedges & Company in 2018, it turns out the average Tesla owner is a 54-year-old white man making over $140,000 with no children; so what would it take to change that demographic to more Millennials and Gen Z?

According to Inc, what matters to these generations is not the flashy features that get Boomers and Gen Xers excited. Ypulse found that the younger buyers are more interested in comfort, reliability, and fuel efficiency – in that order, facets that are offered perhaps by Tesla but which traditional brands offer too, at a lower or comparable price point. Remember too, that peer reviews get factored in too!

Keeping their product lines aligned with their heritage and yet still seeking to appeal to the potentially more environmentally conscious Gen Z it is interesting then, that a heritage brand like Ford has “gassed” its business up with new and compelling offerings in the hybrid and full EV spaces. They’ve recognized the need and played their brand advantage but they’ve done it with data to back up their position.

What this is demonstrative of, is a well-established brand responding relatively quickly and introducing horizontal differentiation within the brand vertical that Ford is. The F-150 Lightning for the utilitarian persona perhaps, the Mustang Mach-E for those looking for something with wholly visual and performance appeal, and the E-Transit for the commercial sector. Three distinctive models for likely entirely different markets.

It is suggested that the Gen Z consumer distinguishes themself from other generations by being perhaps more emotional in terms of needs. There’s an element of their outward appearance and overt visible behavior, combined with a need to be distinctive and unique in the face of a great deal of societal homogeneity.

It is perhaps that reason that singles out their relative disinterest in the Tesla brand. Theirs is a personalized aesthetic perhaps, and the older more established brands like Ford, GM, Nissan, and Toyota et al may actually have a better shot at servicing this aesthetic as a result of having offered personalized customization in the past, while at the same time being ecologically and environmentally responsible. It is also about availability. Do I buy something I can drive off the lot today or do I wait? Time will tell. There’s still a relative scarcity factor a long waitlist for Tesla and that doesn’t help.

So what made Ford, take the ambitious objective of all-electric engineering on at the time that it did? According to Forbes contributor Dale Buss, “Ford futurist”, Sheryl Connelly looked at a global survey of thousands of consumers and articulated in Looking Further with Ford Trends Report that off-planet travel for leisure and entertainment Gen Z; and disinterest in next generations of humanity characterized Gen Z. This is backed up by 81% of adults saying climate change is a worry for future generations and at least 40% of Canadian women, for example, cited as having concerns about climate change as a reason for not wanting to have children.

You could read that as potentially a story that suggests previous generations have ruined the planet and we need to look for alternatives – something not dissimilar to the message in the apocalyptic black comedy film “Don’t Look Up“. It’s conceivable that Gen Z believes that there are well-progressed invisible plans to colonize other planets and not everyone knows or is “in” on those plans.

You can read more on Connelly’s opinion and discoveries here.

Now you might ask, what does this all have to do with Customer Master Data and MDM in particular? Pretectum’s view is that there’s actually quite a lot. Companies that embrace authenticity will find that Gen Z customers will be their best brand ambassadors. There’s no better way to demonstrate authenticity than to be environmentally and socially responsible and yet support uniqueness, individualization, and personalization.

Accommodating individual preferences was ironically the aspect of Henry Ford’s pushback with the Model T that he is well known for. “Any customer can have a car painted any color that he wants, so long as it is black.” The Model T only came in black because the production line required compromise so that efficiency and improved quality could be achieved; modern vehicle production doesn’t have to be so rigid, and in fact, make-to-order (MTO) is increasingly commonplace today even at Ford.

Ford’s goal is to have MTO factory orders account for upwards of a quarter of vehicle sales and consequently, they can reduce overall finished vehicle inventory in US dealerships from a historic average of 75 days to a targeted range of 50-to-60 days.

So, if your business is in automotive for example, you likely have a generation or in fact, multiple generations of buyers of your vehicles. If your customers have had a good past buying and driving experience, they may even have bought multiple vehicles from you.

Those Gen Z’ers that chase a Ford likely grew up in a household where a Ford was owned. There may even be a degree of unintentional brand loyalty among them. As an auto manufacturer, the chances are, you know who those past buyers were, but more importantly, if you’ve afforded them credit, insurance, warranty, extended warranty, and even service and support you know heaps about them from all that interaction.

To maintain those relationships with past customers, and to benefit from the data you have collected in the past you need to consider that you can harvest unique insight that newcomers cannot. You can start to leverage the adequacy of responses to surveys for example. You have to be engaged with those past and present customers and keep them interested in actually engaging with you again and one of the best ways is through personalized communications which are driven by detailed and appropriate customer master data. You can only really do that if the data is aligned with your business needs and the intentions that you have in mind for your outreach programs.

This is where Pretectum as a customer master data management platform provider (CMDM) can be of help. 

    Where to Start Building Data Strategy for a Small Business


    Photo by JESHOOTS.COM on Unsplash Depending on your outlook, your small business might be inspired or intimidated by data science. No matter which end of the spectrum you’re on, one thing you can’t afford to do is ignore it. Data may be the single most important thing you can use to boost profits and cut…
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    The post Where to Start Building Data Strategy for a Small Business appeared first on Seattle Data Guy.


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    Author: research@theseattledataguy.com

    5 Uses Cases for Hybrid Cloud Data Management
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    With the rise of cloud computing, many organizations are opting to use a hybrid approach to their data management. Even though many companies still rely on on-premises storage, the benefits of having cloud storage as a backup or disaster recovery plan can be significant. This post will give you five of the most popular use cases for hybrid cloud data management.

    Why Hybrid Cloud Data Management?

    Hybrid cloud data management isn’t a new concept, but it’s finally starting to hit its stride as a viable option for enterprise data management.  It utilizes a mixture of on-premises and cloud storage and cloud computing to handle all aspects of a company’s data needs. Often, it’s the merger of on-premises databases or enterprise data warehouses (EDW) with cloud storage, SaaS application data and/or a cloud data warehouse (CDW). The benefits of this hybrid approach are twofold: it provides a backup plan for disaster recovery situations, and it gives an organization the ability to scale up as needed without purchasing additional hardware.

    Backup and Disaster Recovery

    One of the most obvious benefits of hybrid cloud data management is that it provides a backup for your data. If your on-premises storage system fails or you lose some important data, you can rely on your cloud storage to get it back. It will act as an additional fail-safe plan in case anything happens to your on-site server.

    Data Accessibility

    Data is not just one homogeneous entity. Many companies can feel hampered by data access. They may not have the in-house expertise or budget to handle the IT demands of data storage and real time access. Through a hybrid cloud environment, your business can access data and applications stored in both on-premises and off-site locations. Global companies can store data closer to applications or users to improve processing time and reduce latency without having to have local data centers or infrastructure.

    Data Analytics

    Currently, many businesses are combining internal data sources with external data sources from partners or public sources for improved analytics. A hybrid data warehouse can allow data teams to combine this third-party data with internal data sources to gain greater insights for decision making. Data engineers can reduce the amount of effort required to source and combine data needed for users to explore new analytical models.

    Data Migration

    When an organization migrates their storage to the cloud, they can take advantage of public, private, and hybrid cloud solutions. This means utilizing a host of services, including backup storage, disaster recovery solutions, analytics, and more. All while paying less money on infrastructure costs and avoiding large capital expenses.

    Data Compliance

    The adoption of a hybrid data warehouse can relieve some of the compliance burdens that can often accompany stored data. For example, retired systems may leave behind orphaned databases, often with useful, historic data. This can create a data gap for analytic teams, but it can also pose a security and compliance risk for the business. Cloud service providers have teams of experts that work with governments and regulators globally to develop standards for things such as data retention times and security measures. Additionally, leveraging the cloud for data storage can also help address the challenges of data residency and data sovereignty regulations, which can become complex as data moves across geographical boundaries.

    Regardless of where you are on your cloud journey, data is the most valuable asset to any organization. The cloud is an increasingly important component as businesses look for ways to leverage their data assets to maintain competitive advantage. Learn more about how the Avalanche Data Platform is helping organizations unlock more value from their data.

    The post 5 Uses Cases for Hybrid Cloud Data Management appeared first on Actian.


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    Author: Traci Curran

    Dear Laura: What Role Should Leadership Play in Data Governance?


    Welcome to the Dear Laura blog series! As I’ve been working to challenge the status quo on Data Governance – I get a lot of questions about how it will “really” work. I’ll be sharing these questions and answers via this DATAVERSITY® series. In 2019, I wrote the book “Disrupting Data Governance” because I firmly believe that […]

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    Author: Laura Madsen

    Top 3 Process Automation Challenges (and How to Solve Them)


    Now is about the time of year when people start rethinking their New Year’s resolutions. Regardless of how great the outcome would have been, perhaps their goals were too ambitious or vague to be reachable. The same holds true for major digital transformation efforts, including process automation.  In a recent survey, nine out of 10 IT decision-makers said process automation helped […]

    The post Top 3 Process Automation Challenges (and How to Solve Them) appeared first on DATAVERSITY.


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    Author: Jakob Freund

    The Blame Game Undermines the Benefits of Cloud Adoption


    It’s human nature to want to point the finger at someone else when something goes wrong, but even if responsibility rightfully lies elsewhere, that’s an ineffective way to solve a problem. Sure, root cause analysis requires identifying the source of an issue, but all too often, we stop short at the person or group exhibiting […]

    The post The Blame Game Undermines the Benefits of Cloud Adoption appeared first on DATAVERSITY.


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    Author: Anthony Cusimano

    AI Ethics: Risk and Opportunity


    Risks often dominate our discussions about the ethics of artificial intelligence (AI), but we also have an ethical obligation to look at the opportunities. In my second article about AI ethics, I argue there is a way to link the two.  “Our future is a race between the growing power of technology and the wisdom with […]

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    Author: Saara HyvĂśnen

    How Cloud Governance Allows Businesses to Become Compliant Superheroes


    In the battle of the old vs. the new, it is evident that traditional identity and access management (IAM) solutions are gradually getting phased out by cloud solutions. Hence, there is a need to shift to cloud-based identity governance and administration (IGA) solutions. This type of cloud governance is more secure and reliable while providing […]

    The post How Cloud Governance Allows Businesses to Become Compliant Superheroes appeared first on DATAVERSITY.


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    Author: Deepak Gupta

    3 Strategies for Creating a Successful MLOps Environment


    Disconnects between development, operations, data engineers, and data science teams might be holding your organization back from extracting value from its artificial intelligence (AI) and machine learning (ML) processes. In short, you may be missing the most essential ingredient of a successful MLOps environment: collaboration. For instance, your data scientists might be using tools like JupyterHub or […]

    The post 3 Strategies for Creating a Successful MLOps Environment appeared first on DATAVERSITY.


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    Author: Will McGrath

    Open Source and Security: How Can We Improve?


    Over the past few months, the open-source community has seen several critical events that have led to big questions about the security and safety of open-source software. How can we evaluate what is currently taking place around open-source projects and security, how can we make these projects more sustainable, and what should we do in […]

    The post Open Source and Security: How Can We Improve? appeared first on DATAVERSITY.


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    Author: Matt Yonkovit

    8 Steps to Put Hyperautomation into Practice


    In recent months there has been a great deal of hype about the concept of hyperautomation. The topic is widely cited as one of the data trends to watch in 2022 and has sparked numerous debates, comments, and blog posts, including my own.   Yet although there has been much thinking and talking, the time has come […]

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    Author: Mathias Golombek

    Forthcoming AI Regulation Makes Data Management Imperative


    Although algorithmic decision-making has become increasingly vital for many businesses, there are growing concerns related to transparency and fairness. To put it mildly, the concern is warranted. Not only has there been documentation of racial bias in facial recognition systems, but algorithmic decision-making has also played a role in denying minorities home loans, prioritizing men during hiring, […]

    The post Forthcoming AI Regulation Makes Data Management Imperative appeared first on DATAVERSITY.


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    Author: Ramprakash Ramamoorthy

    10 Key Data Mining Challenges in NLP and Their Solutions


    Even as we grow in our ability to extract vital information from big data, the scientific community still faces roadblocks that pose major data mining challenges. In this article, we will discuss 10 key issues that we face in modern data mining and their possible solutions. 1. Heterogeneous Data Data can be of low quality, […]

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    Author: Martin Ostrovsky

    Consolidation Renews the Fight for Independence


    They say that history repeats itself, and that those who don’t remember the past are doomed to repeat it. We’re about to see another instance – with regards to consolidation – where these old adages ring true. Years ago, at the start of the cloud revolution, companies began to flee from the idea of a monolithic, […]

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    Author: Craig Stewart

    Does Your Organization Have A Data Platform Leader? It Could Soon.
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    There’s no one-size-fits-all solution for a modern data platform, and there likely never will be with the proliferation of multiple public and private cloud environments, entrenched on-premises data centers, and the exponential rise in edge computing – data sources are multiplying almost at the rate of data itself.

    Today’s data platforms increasingly take a broad multi-platform approach that incorporates a wide range of data services (e.g. data warehouse, data lake, transactional database, IoT database and third-party data services),  and integration services that support all major clouds and on-premise platforms and applications that run on and across these environments. Modern data platforms need a data fabric – technology that enables data that is distributed across different areas to be accessed in real-time in a unifying data layer,  – to drive data flow orchestration, data enrichment, and automation To meet the varied requirements of users across an organization including data engineers, data scientists, business analysts and business users, the platform should also incorporate shared management and security services, as well as support a wide range of application development and analytical tools.

    However, these needs create a singular challenge: who’s going to manage the creation and maintenance of such a platform? That’s where the role of the platform leader comes in. Just as we’ve seen the creation of roles like Chief Data Officer and Chief Diversity Officer in response to critical needs, organizations require a highly skilled individual to manage the creation and maintenance of their platform(s). Enter the data platform leader – someone with a broad understanding of databases and streaming technologies, as well as a practical understanding of how to facilitate frictionless access to these data sources, how to formulate a new purpose, vision and mission for the platform and how to form close partnerships with analytics translators.  We’ll get to those folks in a minute.

    Developing a New Purpose, Vision and Mission

    Why must a data platform leader develop a new purpose, vision and mission? Consider this: data warehouse users have traditionally been data engineers, data scientists and business analysts who are interested in complex analytics. These users typically represent a relatively small percentage of an organization’s employees. The power and accessibility of a data platform capable of running not just in the data center, but also in the cloud or at the edge, will invariably bring in a broader base of business users who will use the platform to run simpler queries and analytics to make operational decisions.

    However, accompanying these users will be new sets of business and operational requirements. To satisfy this ever-expanding user base and their different requirements, the data platform leader will need to formulate a new purpose for the platform (why it exists), a new vision for the platform (what it hopes to deliver) and a new mission (how will it achieve the vision).

    Facilitating Data Service Convergence

    Knowledge of relational databases with analytics-optimized schemas and/or analytic databases has long been part of a data warehouse manager’s wheelhouse. However, the modern data platform extends access much further, enabling access to data lakes and transactional and IoT databases, and even streaming data. Increasing demand for real-time insights and non-relational data that can enable decision intelligence are bringing these formerly distinct worlds closer together. This requires the platform leader to have a broad understanding of databases and streaming technologies as well as a practical understanding of how to facilitate frictionless access to these data sources.

    Enabling Frictionless Data Access

    A data warehouse typically includes a semantic layer that represents data so end users can access that data using common business terms. A modern data platform, though, demands more. While a semantic layer is valuable, data platform leaders will need to enable more dynamic data integration than is typically sufficient to support a centralized data warehouse design.  Enter the data fabric to provide a service layer that enables real-time access to data sourced from the full range of the data platform’s various services. The data fabric offers frictionless access to data from any source located on-premises and in the cloud to support the wide range of analytic and operational use cases that such a platform is intended to serve.

    Working with Analytics Translators

    I mentioned earlier that data platform leaders would need the ability to form close partnerships with analytics translators. Let’s start with what an analytics translator does and then we’ll get to why a close relationship is important.

    According to McKinsey & Company, the analytics translator serves the following purpose:

    “At the outset of an analytics initiative, translators draw on their domain knowledge to help business leaders identify and prioritize their business problems, based on which will create the highest value when solved. These may be opportunities within a single line of business (e.g., improving product quality in manufacturing) or cross-organizational initiatives (e.g., reducing product delivery time).”

    I expect the analytics translator and the data platform leader will become important partners. The analytics translator will be invaluable in establishing data platform priorities, and the platform leader will provide the analytics translator with key performance indicators (KPIs) on mutually-agreed-upon usage goals.

    In conclusion, the data platform leader has many soft and hard skillset requirements in common with a data warehouse manager, but there are a few fundamental and significant differences. The key difference includes developing a new purpose, vision and mission, having expertise in new data services and data fabrics, knowing how best to access those services, and possessing the ability to form close partnerships with analytics translators.

     

    The post Does Your Organization Have A Data Platform Leader? It Could Soon. appeared first on Actian.


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    Author: Teresa Wingfield

    Recommendations to Level Up Your Machine Learning Platform


    With machine learning (ML) and artificial intelligence (AI) applications becoming more business-critical, organizations are in the race to advance their AI/ML capabilities. To realize the full potential of AI/ML, having the right underlying machine learning platform is a prerequisite. Today’s machine learning platforms are undergoing rapid, fundamental innovations at an architectural level. Meanwhile, organizations are […]

    The post Recommendations to Level Up Your Machine Learning Platform appeared first on DATAVERSITY.


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    Author: Bin Fan

    What the Hybrid, Multi-Cloud Resurgence Means for the Cloud Landscape


    As a result of the global pandemic, flexibility has become critically important to enterprises – and far more common in business operations than ever before. The vast majority of companies have instituted flexible working policies to retain and attract employees; behind the scenes, they have also increasingly adopted cloud services to enable agility during uncertain […]

    The post What the Hybrid, Multi-Cloud Resurgence Means for the Cloud Landscape appeared first on DATAVERSITY.


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    Author: Ashish Yajnik

    The Semantic Layer Goes Mainstream


    The semantic layer concept within the data stack is not new but is an increasingly popular topic of conversation. I predict that in 2022, we’ll see mainstream awareness of the semantic layer, especially as enterprises begin to see real-world examples of its benefits.  The fact that industry leaders are discussing the need for a semantic […]

    The post The Semantic Layer Goes Mainstream appeared first on DATAVERSITY.


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    Author: David Mariani

    How Analytics Shapes Our Music Tastes in the Era of Digital Streaming


    Next in our blog series exploring interesting analytics use cases, we examine how machine learning algorithms dictate the music we listen to every day.  In 2019, the music streaming market was valued at $12,831.2 million – a figure that’s expected to nearly double by 2027. Music streaming has become the most popular medium for music consumption, significantly outperforming […]

    The post How Analytics Shapes Our Music Tastes in the Era of Digital Streaming appeared first on DATAVERSITY.


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    Author: Nitin Aggarwal

    How To Accelerate Data Democratization
    The future of data democratization lies in the hands of your end-users. They are already generating data, they know what problems they need to solve with it, and they know how to use that information for business value. It is time you let the end-users pull their own weight without having to rely on IT […]


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    Author: Ben Herzberg

    The Data Behind Love
    Relationship status aside, it’s hard to avoid Valentine’s Day: from your apps to in-store campaigns to cheesy e-cards filling up your inbox, it’s everywhere. You may spend time with a loved one, over-indulge in your favorite chocolate, or make a mockery of the whole thing and stock up on clearance candy on the 15th. Whatever […]


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    Author: Cheryl To

    Data Speaks for Itself: Data Littering
    No, this is not a mistyping of data literacy. Yes, like everyone, I am aware of and fully on-board with the growing movement to improve data literacy in the enterprise. What I want to talk about is Data Littering, which is something else entirely. Data Littering is the deliberate act of creating and distributing data […]


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    Author: Dr. John Talburt

    Zen and the Art of Data Maintenance: Don’t Integrate, Don’t Separate – Indegrate
    One of the most common and important dialogues is when the enterprise data architect expresses the need to integrate and the project manager is completely focused on developing their specific application. The following type of conversation will often happen: Enterprise Data architect for a large company: “We have been asked to help on this project […]


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    Author: Len Silverston

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